基于Connectome的早期和慢性精神病症状的预测建模
Maya L Foster1, Jean Ye2, Albert R Powers3,4
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA. maya.foster@yale.edu.
概括
慢性精神病 (CP) 和早期精神病 (EP) 分享大脑网络,但CP显示出更强大的症状预测建模效应. 在这两种研究中,前端双层网络是关键,为未来的精神病研究提供了信息.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 脑部成像 脑部成像
背景情况:
- 早期精神病 (EP) 和慢性精神病 (CP) 分享神经相关性和症状.
- 临床差异表明有不同的潜在大脑网络,但直接比较是有限的.
- 了解这些差异对于有针对性的治疗应用至关重要.
研究的目的:
- 直接比较CP和EP症状的神经相关物.
- 用先进的建模研究EP和CP之间脑网络连接的差异.
- 识别关键的大脑网络与精神病症状严重程度相关的疾病阶段.
主要方法:
- 在大批CP (n=123) 和EP (n=107) 患者中利用休息状态功能磁共振成像 (rs-fMRI).
- 应用基于连接组的预测建模 (CPM) 来使用正负综合征量表 (PANSS) 预测症状严重程度.
- 进行虚拟损伤分析以识别关键网络组件.
主要成果:
- 预测建模显示,与EP相比,CP的症状预测效果大小更高.
- 预测一般的精神病理和总体症状仅在CP组中是可行的.
- 确定前端双层网络是EP和CP症状网络中的关键组成部分.
- 预测模型在各组中基本相似,在CP中观察到更大的效应.
结论:
- 虽然基本上类似的大脑网络是EP和CP的基础,但预测能力的差异表明不同的网络动态.
- 面对对面的网络在精神病症状网络中发挥着重要作用,跨越疾病阶段.
- 这些发现支持了阶段比较研究的必要性,以了解精神病的病因学,并为治疗策略提供信息.
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